Titlebook: Artificial Intelligence and Robotics; 8th International Sy Huimin Lu,Jintong Cai Conference proceedings 2024 The Editor(s) (if applicable)

MOAN

粘

Improving Road Extraction in Hyperspectral Data with Deep Learning Models,clusion, changing lighting conditions, and blur. To address this issue, this paper proposes a new model that combines the advantages of U-net and Transformer architectures. This hybrid model effectively captures both local and long-range features, thus improving the accuracy and efficiency of road e

Hectic

Human Related Information Extraction from Chinese Archive Images,racting and managing image content from archives is still in its early stages, and is primarily focused on recognizing fixed-format archive images. As a result, there is a lack of technology for extracting key personal information applicable to all types of archive images. To address this, we have i

刺激

Multi-virtual View Scoring Network for 3D Hand Pose Estimation from a Single Depth Image,al view depth images. By projecting a single depth image through point cloud transformation and using the depth images of multiple virtual views together for hand pose estimation, these methods can effectively improve the estimation accuracy. However, current methods have issues with distorted gener

sundowning

Digital Archive Stamp Detection and Extraction, water, fire, and mold, making long-term storage difficult. To address this issue, digital archives have been established for management. As a result, effective storage, detection, extraction, and utilization of archive information has become a focus of attention. This paper focuses on the feature e

Sciatica

輕彈

偽證

Improved GR-Convnet for Antipodal Robotic Grasping,level approach of GR-Convnet for the task and propose a neural network with high robustness while maintaining real-time performance. The three improvements include introducing Squeeze and Excitation (SE) blocks, removing Dropout in the final layer, and using Residual Block and Concurrent Spatial and

勉強(qiáng)

CS-Net: A Stain Style Transfer Network for Histology Images with CS-Gate Attention,thods on the synthesized IHC-stained images to obtain the final binary mask of the OPC epithelium. The experimental results show that CS-Net can synthesize more stable images with a higher degree of restoration than the GAN-like networks, and the accuracy of the final obtained mask is also better th

共同確定為確